@inproceedings{pub8141, title = {Modeling Complex Event Patterns in EPC-Models and Transforming them into an Executable Event Pattern Language}, author = {Julian Krumeich and Manuel Peter Zapp and Dirk Mayer and Dirk Werth and Peter Loos}, editor = {Dirk Stelzer and Volker Nissen and Steffen Straßburger}, url = {http://www.db-thueringen.de/servlets/DerivateServlet/Derivate-33063/ilm1-2016100012.pdf}, year = {2016}, date = {2016-01-01}, booktitle = {Multikonferenz Wirtschaftsinformatik (MKWI 2016), Ilmenau, Germany, March 2016}, volume = {1}, publisher = {tba}, abstract = {This paper proposes an approach to model complex event patterns in Event-driven Process Chain (EPC) models and to transform them into an executable Event Pattern Language (EPL). To do this, the paper first of all derives and examines characteristic event patterns considered in corresponding literature. Afterwards, the feasibility to model them using the standard EPC metamodel is evaluated and an extended EPC metamodel is proposed that allows for a comprehensive depiction of the derived patterns. Based on this foundation, the paper conceives a modeling technique and corresponding notation using the ARIS method and integrates it into the ARIS Business Process Analysis Platform for tooling support. Moreover, the paper proposes an approach that automatically transforms these models into executable EPL using Abstract Syntax Trees as an intermediate representation. Finally, the paper illustrates the modelling, extraction and transformation steps based on a running example originating from the ongoing research project (blinded) within continuous casting in the steel manufacturing industry.}, note = {WKWI: C, VHB: D}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} }

This paper proposes an approach to model complex event patterns in Event-driven Process Chain (EPC) models and to transform them into an executable Event Pattern Language (EPL). To do this, the paper first of all derives and examines characteristic event patterns considered in corresponding literature. Afterwards, the feasibility to model them using the standard EPC metamodel is evaluated and an extended EPC metamodel is proposed that allows for a comprehensive depiction of the derived patterns. Based on this foundation, the paper conceives a modeling technique and corresponding notation using the ARIS method and integrates it into the ARIS Business Process Analysis Platform for tooling support. Moreover, the paper proposes an approach that automatically transforms these models into executable EPL using Abstract Syntax Trees as an intermediate representation. Finally, the paper illustrates the modelling, extraction and transformation steps based on a running example originating from the ongoing research project (blinded) within continuous casting in the steel manufacturing industry.

@article{pub7822, title = {Prescriptive Control of Business Processes - New Potentials through Predictive Analytics on Big Data in the Process Manufacturing Industry}, author = {Julian Krumeich and Dirk Werth and Peter Loos}, url = {http://link.springer.com/article/10.1007/s12599-015-0412-2}, year = {2016}, date = {2016-01-01}, journal = {Business & Information Systems Engineering (BISE)}, volume = {58}, number = {4}, pages = {261-280}, publisher = {Springer, Wiesbaden}, abstract = {This paper proposes a concept for a prescriptive control of business processes by using event-based process predictions. In this regard, it explores new potentials through the application of predictive analytics on big data while focusing on production planning and control in the context of the process manufacturing industry. This type of industry is an adequate application domain of the conceived concept, since it features several characteristics that are opposed to conventional industries such as assembling ones. These specifics include divergent and cyclic material flows, high diversity in end products’ qualities, as well as non-linear production processes that are not fully controllable. Based on a case study of a German steel producing company—which is a typical example of the process industry—the work at hand outlines which data is becoming available when using state-of-the-art sensor technology and thus providing the required basis to realize the proposed concept. However, a consideration of the data size reveals that dedicated methods of big data analytics are required to tap the full potential of this data. Consequently, the paper derives seven requirements that need to be addressed for a successful implementation of the concept. Additionally, the paper proposes a generic architecture of prescriptive enterprise systems. This architecture comprises five building blocks of a system that is capable to detect complex event patterns within a multi-sensor environment, to correlate them with historical data and to calculate predictions that are finally used to recommend the best course of action during process execution in order to minimize or maximize certain key performance indicators.}, keywords = {}, pubstate = {published}, tppubtype = {article} }

This paper proposes a concept for a prescriptive control of business processes by using event-based process predictions. In this regard, it explores new potentials through the application of predictive analytics on big data while focusing on production planning and control in the context of the process manufacturing industry. This type of industry is an adequate application domain of the conceived concept, since it features several characteristics that are opposed to conventional industries such as assembling ones. These specifics include divergent and cyclic material flows, high diversity in end products&#8217; qualities, as well as non-linear production processes that are not fully controllable. Based on a case study of a German steel producing company&#8212;which is a typical example of the process industry&#8212;the work at hand outlines which data is becoming available when using state-of-the-art sensor technology and thus providing the required basis to realize the proposed concept. However, a consideration of the data size reveals that dedicated methods of big data analytics are required to tap the full potential of this data. Consequently, the paper derives seven requirements that need to be addressed for a successful implementation of the concept. Additionally, the paper proposes a generic architecture of prescriptive enterprise systems. This architecture comprises five building blocks of a system that is capable to detect complex event patterns within a multi-sensor environment, to correlate them with historical data and to calculate predictions that are finally used to recommend the best course of action during process execution in order to minimize or maximize certain key performance indicators.

Enterprises in today&#8217;s globalized world are compelled to react on threats and opportunities in a highly flexible manner. Due to technological advancements, real-time information availability, especially in manufacturing operations, has reached new dimensions and increasingly provides Big Data. With Complex Event Processing (CEP) the required technology to analyze and correlate heterogeneous event data is already available. Yet, these techniques are only scattered applied to Predictive Analytics, especially in the Event-driven Business Process Management domain. Most approaches are based on pure descriptive analytics not considering current context-situations appropriately. To enact a stronger situation-awareness, the paper at hand proposes the concept of event-based process predictions combining CEP with Predictive Analytics and outlines its potentials in particular for a proactive control of manufacturing processes.

@inproceedings{pub7335, title = {Examining Existing Ways to Electronically Declare International Exports to the German ATLAS System - Current Barriers and Proposed Solution}, author = {Julian Krumeich and Dirk Werth and Peter Loos}, url = {http://aisel.aisnet.org/amcis2014/eGovernment/GeneralPresentations/10/}, year = {2014}, date = {2014-08-01}, booktitle = {20th Americas Conference on Information Systems (AMCIS 2014), Savannah, GA, USA, August 2014}, publisher = {AIS Electronic Library}, abstract = {With the ongoing globalization an increase in international deliveries of goods goes hand in hand. All goods produced within a customs union and leave this territory, must be transferred to an export procedure associated with a required customs clearance. This is frequently perceived as an obstacle in the export process. According to a United Nations study, an inefficient customs clearance is responsible for 7% of international trade costs. Hence, governments across the world aim at reducing this administrative effort by introducing electronic customs systems. The paper examines existing ways to electronically declare exports using the example of the German ATLAS system. Based on six selection criteria for enterprise software, these ways are examined regarding their appropriateness from the perspective of small and medium-sized enterprises (SME). The paper concludes that none of them meet sufficiently SME-specific requirements. To tackle this, the paper presents the research project EXPORT and its prototypical implementation.}, note = {WKWI: B, VHB: D}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} }

With the ongoing globalization an increase in international deliveries of goods goes hand in hand. All goods produced within a customs union and leave this territory, must be transferred to an export procedure associated with a required customs clearance. This is frequently perceived as an obstacle in the export process. According to a United Nations study, an inefficient customs clearance is responsible for 7% of international trade costs. Hence, governments across the world aim at reducing this administrative effort by introducing electronic customs systems. The paper examines existing ways to electronically declare exports using the example of the German ATLAS system. Based on six selection criteria for enterprise software, these ways are examined regarding their appropriateness from the perspective of small and medium-sized enterprises (SME). The paper concludes that none of them meet sufficiently SME-specific requirements. To tackle this, the paper presents the research project EXPORT and its prototypical implementation.

@inproceedings{pub7279, title = {Big Data Analytics for Predictive Manufacturing Control - A Case Study from Process Industry}, author = {Julian Krumeich and Sven Jacobi and Dirk Werth and Peter Loos}, editor = {Peter Chen and Hemant Jain}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6906825&newsearch=true&searchWithin=%22First%20Name%22:dirk&searchWithin=%22Last%20Name%22:werth}, isbn = {978-1-4799-5057-7}, year = {2014}, date = {2014-06-01}, booktitle = {3rd IEEE International Congress on BigData (BigData 2014), Anchorage, Alaska, June 2014}, pages = {530-537}, publisher = {IEEE Xplore® Digital Library}, organization = {Institute of Electrical and Electronics Engineers}, abstract = {To keep up with increasing market demands in global competition, companies are forced to dynamically adapt each of their business process executions to individual business situations. Companies that are able to analyze the current states of their business processes, forecast their most optimal progress and proactively control them based on the derived knowledge, are an essential step ahead competitors. The paper at hand exploits the potentials through the usage of predictive analytics on big data aiming at event-based forecasts and proactive control of business processes. In doing so, the paper outlinesbased on a case study of a large steel producing companywhich production-related data can be collected by applied sensor technology at present; hence, forming a potential foundation for accurate forecasts. However, without dedicated methods of big data analytics, the company cannot utilize the potential of already available data for a proactive process control. Hence, the article forms a working and discussion basis for further research in big data analytics.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} }

To keep up with increasing market demands in global competition, companies are forced to dynamically adapt each of their business process executions to individual business situations. Companies that are able to analyze the current states of their business processes, forecast their most optimal progress and proactively control them based on the derived knowledge, are an essential step ahead competitors. The paper at hand exploits the potentials through the usage of predictive analytics on big data aiming at event-based forecasts and proactive control of business processes. In doing so, the paper outlines&#151;based on a case study of a large steel producing company&#151;which production-related data can be collected by applied sensor technology at present; hence, forming a potential foundation for accurate forecasts. However, without dedicated methods of big data analytics, the company cannot utilize the potential of already available data for a proactive process control. Hence, the article forms a working and discussion basis for further research in big data analytics.

This paper proposes a conceptual approach for interactive process discovery and improvement in people-driven business process. Developed as a supplementary element to traditional process mining, it addresses two recently identified challenges of process mining highlighted in the Process Mining Manifesto by the IEEE Task Force on Process Mining. The developed approach guides users through the execution of business processes via recommendations based on underlying process models, which can result from traditional process mining techniques. Specifically, the approach provides recommendations learned from previous processes executed by a particular user and couples them with decisions taken from all users involved in that business process. The individual process executions eventually feed back into the overall process model enabling process model evolutions and hence living process models.